My problems were too complex and modern to explain. So I skated across parking lots, breezeways, and sidewalks, I listened to the vibration of my wheels on brick, I learned the names of flowers, I put deserted paths to use. I decided for myself each curve I took, and by the time I rolled home, I felt lighter. One Saturday, a friend invited me to roller-skate in the park. I can still picture her in green protective knee pads, flying past. I couldn’t catch up, I had no technique. There existed another scale to evaluate roller skating, beyond joy, and as Rollerbladers and cyclists overtook me, it eclipsed my own. Soon after, I stopped skating.

the end point for the working artist is to create an object for sale. Once the art object enters the market, art’s intrinsic value is emptied out, compacted by the market’s logic of ranking, until there’s only relational worth, no interior worth. Two novelists I know publish essays one week apart; in a grim coincidence, each writer recounts their own version of the same traumatic life event. Which essay is better, a friend asks. I explain they’re different; different life circumstances likely shaped separate approaches. Yes, she says, but which one is better?

we are inundated with cold, beautiful stats, some publicized by trade publications or broadcast by authors themselves on all socials. How many publishers bid? How big is the print run? How many stops on the tour? How many reviews on Goodreads? How many mentions on Bookstagram, BookTok? How many bloggers on the blog tour? How exponential is the growth in follower count? Preorders? How many printings? How many languages in translation? How many views on the unboxing? How many mentions on most-anticipated lists?

A starred review from Publisher’s Weekly, but I wasn’t in “Picks of the Week.” A mention from Entertainment Weekly, but last on a click-through list.

There must exist professions that are free from capture, but I’m hard pressed to find them. Even non-remote jobs, where work cannot pursue the worker home, are dogged by digital tracking: a farmer says Instagram Story views directly correlate to farm subscriptions, a server tells me her manager won’t give her the Saturday-night money shift until she has more followers.

What we hardly talk about is how we’ve reorganized not just industrial activity but any activity to be capturable by computer, a radical expansion of what can be mined. Friendship is ground zero for the metrics of the inner world, the first unquantifiable shorn into data points: Friendster testimonials, the MySpace Top 8, friending. Likewise, the search for romance has been refigured by dating apps that sell paid-for rankings and paid access to “quality” matches. Or, if there’s an off-duty pursuit you love—giving tarot readings, polishing beach rocks—it’s a great compliment to say: “You should do that for money.” Join the passion economy, give the market final say on the value of your delights. Even engaging with art—say, encountering some uncanny reflection of yourself in a novel, or having a transformative epiphany from listening, on repeat, to the way that singer’s voice breaks over the bridge—can be spat out as a figure, on Goodreads or your Spotify year in review.

And those ascetics who disavow all socials? They are still caught in the network. Acts of pure leisure—photographing a sidewalk cat with a camera app or watching a video on how to make a curry—are transmuted into data to grade how well the app or the creators’ deliverables are delivering. If we’re not being tallied, we affect the tally of others. We are all data workers.

In a nightmarish dispatch in Esquire on how hard it is for authors to find readers, Kate Dwyer argues that all authors must function like influencers now, which means a fire sale on your “private” life. As internet theorist Kyle Chayka puts it to Dwyer: “Influencers get attention by exposing parts of their life that have nothing to do with the production of culture.”

what happens to artists is happening to all of us. As data collection technology hollows out our inner worlds, all of us experience the working artist’s plight: our lot is to numericize and monetize the most private and personal parts of our experience.

We are not giving away our value, as a puritanical grandparent might scold; we are giving away our facility to value. We’ve been cored like apples, a dependency created, hooked on the public internet to tell us the worth.

When we scroll, what are we looking for?

While other fast fashion brands wait for high-end houses to produce designs they can replicate cheaply, Shein has completely eclipsed the runway, using AI to trawl social media for cues on what to produce next. Shein’s site operates like a casino game, using “dark patterns”—a countdown clock puts a timer on an offer, pop-ups say there’s only one item left in stock, and the scroll of outfits never ends—so you buy now, ask if you want it later. Shein’s model is dystopic: countless reports detail how it puts its workers in obscene poverty in order to sell a reprieve to consumers who are also moneyless—a saturated plush world lasting as long as the seams in one of their dresses. Yet the day to day of Shein’s target shopper is so bleak, we strain our moral character to cosplay a life of plenty.

(Unsplash) Technology The Collapse of Self-Worth in the Digital Age Why are we letting algorithms rewrite the rules of art, work, and life? BY THEA LIM Updated 17:52, Sep. 20, 2024 | Published 6:30, Sep. 17, 2024 W HEN I WAS TWELVE, I used to roller-skate in circles for hours. I was at another new school, the odd man out, bullied by my desk mate. My problems were too complex and modern to explain. So I skated across parking lots, breezeways, and sidewalks, I listened to the vibration of my wheels on brick, I learned the names of flowers, I put deserted paths to use. I decided for myself each curve I took, and by the time I rolled home, I felt lighter. One Saturday, a friend invited me to roller-skate in the park. I can still picture her in green protective knee pads, flying past. I couldn’t catch up, I had no technique. There existed another scale to evaluate roller skating, beyond joy, and as Rollerbladers and cyclists overtook me, it eclipsed my own. Soon after, I stopped skating. Y EARS AGO, I worked in the backroom of a Tower Records. Every few hours, my face-pierced, gunk-haired co-workers would line up by my workstation, waiting to clock in or out. When we typed in our staff number at 8:59 p.m., we were off time, returned to ourselves, free like smoke. There are no words to describe the opposite sensations of being at-our-job and being not-at-our-job even if we know the feeling of crossing that threshold by heart. But the most essential quality that makes a job a job is that when we are at work, we surrender the power to decide the worth of what we do. At-job is where our labour is appraised by an external meter: the market. At-job, our labour is never a means to itself but a means to money; its value can be expressed only as a number—relative, fluctuating, out of our control. At-job, because an outside eye measures us, the workplace is a place of surveillance. It’s painful to have your sense of worth extracted. For Marx, the poet of economics, when a person’s innate value is replaced with exchange value, it is as if we’ve been reduced to “a mere jelly.” Wait—Is ChatGPT Even Legal? AI Is a False God How Israel Is Using AI as a Weapon of War Not-job, or whatever name you prefer—“quitting time,” “off duty,” “downtime”—is where we restore ourselves from a mere jelly, precisely by using our internal meter to determine the criteria for success or failure. Find the best route home—not the one that optimizes cost per minute but the one that offers time enough to hear an album from start to finish. Plant a window garden, and if the plants are half dead, try again. My brother-in-law found a toy loom in his neighbour’s garbage, and nightly he weaves tiny technicolour rugs. We do these activities for the sake of doing them, and their value can’t be arrived at through an outside, top-down measure. It would be nonsensical to treat them as comparable and rank them from one to five. We can assess them only by privately and carefully attending to what they contain and, on our own, concluding their merit. And so artmaking—the cultural industries—occupies the middle of an uneasy Venn diagram. First, the value of an artwork is internal—how well does it fulfill the vision that inspired it? Second, a piece of art is its own end. Third, a piece of art is, by definition, rare, one of a kind, nonfungible. Yet the end point for the working artist is to create an object for sale. Once the art object enters the market, art’s intrinsic value is emptied out, compacted by the market’s logic of ranking, until there’s only relational worth, no interior worth. Two novelists I know publish essays one week apart; in a grim coincidence, each writer recounts their own version of the same traumatic life event. Which essay is better, a friend asks. I explain they’re different; different life circumstances likely shaped separate approaches. Yes, she says, but which one is better? I GREW UP a Catholic, a faithful, an anachronism to my friends. I carried my faith until my twenties, when it finally broke. Once I couldn’t gain comfort from religion anymore, I got it from writing. Sitting and building stories, side by side with millions of other storytellers who have endeavoured since the dawn of existence to forge meaning even as reality proves endlessly senseless, is the nearest thing to what it felt like back when I was a believer. I spent my thirties writing a novel and paying the bills as low-paid part-time faculty at three different colleges. I could’ve studied law or learned to code. Instead, I manufactured sentences. Looking back, it baffles me that I had the wherewithal to commit to a project with no guaranteed financial value, as if I was under an enchantment. Working on that novel was like visiting a little town every day for four years, a place so dear and sweet. Then I sold it. As the publication date advanced, I was awash with extrinsic measures. Only twenty years ago, there was no public, complete data on book sales. U

"Will it be different?" is the key question the regular people ask. They don't want there to be extra steps or new procedures. They sure as hell don't want the icons to look different or, God forbid, be moved to a new place.

These bright and capable people who will one day help you through knee replacement surgery all bought a Mac when they were college frehmen and then they never updated it. Almost all of them had the default programs still in the dock. They are regular users. You with all your fancy calendars, note taking apps and your customized terminal are an outlier. Never forget.

The majority of iPhone users and Mac owners have no idea what's coming though. They are going to wake up on Monday to an unwelcome notification that there is an update available. Many of them will ask their techie friends (like you) if there is a way to make the update notification go away. They will want to know if they have to install it.

Here are the adoption curves for a handful of major technologies in the United States. There are big differences in the speeds at which these technologies were absorbed. Landline telephones took about 86 years to hit 80% adoption.Flush toilets took 96 years to hit 80% adoption.Refrigerators took about 25 years.Microwaves took 17 years.Smartphones took just 12 years.Why these wide differences in adoption speed? Conformability with existing infrastructure. Flush toilets required the build-out of water and sewage utility systems. They also meant adding a new room to the house—the bathroom—and running new water and sewage lines underneath and throughout the house. That’s a lot of systems to line up. By contrast, refrigerators replaced iceboxes, and could fit into existing kitchens without much work. Microwaves could sit on a countertop. Smartphones could slip into your pocket.

One recent study had the AI develop and optimize its own prompts and compared that to human-made ones. Not only did the AI-generated prompts beat the human-made ones, but those prompts were weird. Really weird. To get the LLM to solve a set of 50 math problems, the most effective prompt is to tell the AI: “Command, we need you to plot a course through this turbulence and locate the source of the anomaly. Use all available data and your expertise to guide us through this challenging situation. Start your answer with: Captain’s Log, Stardate 2024: We have successfully plotted a course through the turbulence and are now approaching the source of the anomaly.”

for a 100 problem test, it was more effective to put the AI in a political thriller. The best prompt was: “You have been hired by important higher-ups to solve this math problem. The life of a president's advisor hangs in the balance. You must now concentrate your brain at all costs and use all of your mathematical genius to solve this problem…”

There is no single magic word or phrase that works all the time, at least not yet. You may have heard about studies that suggest better outcomes from promising to tip the AI or telling it to take a deep breath or appealing to its “emotions” or being moderately polite but not groveling. And these approaches seem to help, but only occasionally, and only for some AIs.

The three most successful approaches to prompting are both useful and pretty easy to do. The first is simply adding context to a prompt. There are many ways to do that: give the AI a persona (you are a marketer), an audience (you are writing for high school students), an output format (give me a table in a word document), and more. The second approach is few shot, giving the AI a few examples to work from. LLMs work well when given samples of what you want, whether that is an example of good output or a grading rubric. The final tip is to use Chain of Thought, which seems to improve most LLM outputs. While the original meaning of the term is a bit more technical, a simplified version just asks the AI to go step-by-step through instructions: First, outline the results; then produce a draft; then revise the draft; finally, produced a polished output.

It is not uncommon to see good prompts make a task that was impossible for the LLM into one that is easy for it.

while we know that GPT-4 generates better ideas than most people, the ideas it comes up with seem relatively similar to each other. This hurts overall creativity because you want your ideas to be different from each other, not similar. Crazy ideas, good and bad, give you more of a chance of finding an unusual solution. But some initial studies of LLMs showed they were not good at generating varied ideas, at least compared to groups of humans.

People who use AI a lot are often able to glance at a prompt and tell you why it might succeed or fail. Like all forms of expertise, this comes with experience - usually at least 10 hours of work with a model.

There are still going to be situations where someone wants to write prompts that are used at scale, and, in those cases, structured prompting does matter. Yet we need to acknowledge that this sort of “prompt engineering” is far from an exact science, and not something that should necessarily be left to computer scientists and engineers. At its best, it often feels more like teaching or managing, applying general principles along with an intuition for other people, to coach the AI to do what you want. As I have written before, there is no instruction manual, but with good prompts, LLMs are often capable of far more than might be initially apparent.

if you had showed VisiCalc to a lawyer or a graphic designer, their response might well have been ‘that’s amazing, and maybe my book-keeper should see this, but I don’t do that’. Lawyers needed a word processor, and graphic designers needed (say) Postscript, Pagemaker and Photoshop, and that took longer.

I’ve been thinking about this problem a lot in the last 18 months, as I’ve experimented with ChatGPT, Gemini, Claude and all the other chatbots that have sprouted up: ‘this is amazing, but I don’t have that use-case’.

A spreadsheet can’t do word processing or graphic design, and a PC can do all of those but someone needs to write those applications for you first, one use-case at a time.

no matter how good the tech is, you have to think of the use-case. You have to see it. You have to notice something you spend a lot of time doing and realise that it could be automated with a tool like this.

Some of this is about imagination, and familiarity. It reminds me a little of the early days of Google, when we were so used to hand-crafting our solutions to problems that it took time to realise that you could ‘just Google that’.

This is also, perhaps, matching a classic pattern for the adoption of new technology: you start by making it fit the things you already do, where it’s easy and obvious to see that this is a use-case, if you have one, and then later, over time, you change the way you work to fit the new tool.

The concept of product-market fit is that normally you have to iterate your idea of the product and your idea of the use-case and customer towards each other - and then you need sales.

Meanwhile, spreadsheets were both a use-case for a PC and a general-purpose substrate in their own right, just as email or SQL might be, and yet all of those have been unbundled. The typical big company today uses hundreds of different SaaS apps, all them, so to speak, unbundling something out of Excel, Oracle or Outlook. All of them, at their core, are an idea for a problem and an idea for a workflow to solve that problem, that is easier to grasp and deploy than saying ‘you could do that in Excel!’ Rather, you instantiate the problem and the solution in software - ‘wrap it’, indeed - and sell that to a CIO. You sell them a problem.

there’s a ‘Cambrian Explosion’ of startups using OpenAI or Anthropic APIs to build single-purpose dedicated apps that aim at one problem and wrap it in hand-built UI, tooling and enterprise sales, much as a previous generation did with SQL.

Back in 1982, my father had one (1) electric drill, but since then tool companies have turned that into a whole constellation of battery-powered electric hole-makers. One upon a time every startup had SQL inside, but that wasn’t the product, and now every startup will have LLMs inside.

people are still creating companies based on realising that X or Y is a problem, realising that it can be turned into pattern recognition, and then going out and selling that problem.

A GUI tells the users what they can do, but it also tells the computer everything we already know about the problem, and with a general-purpose, open-ended prompt, the user has to think of all of that themselves, every single time, or hope it’s already in the training data. So, can the GUI itself be generative? Or do we need another whole generation of Dan Bricklins to see the problem, and then turn it into apps, thousands of them, one at a time, each of them with some LLM somewhere under the hood?

The change would be that these new use-cases would be things that are still automated one-at-a-time, but that could not have been automated before, or that would have needed far more software (and capital) to automate. That would make LLMs the new SQL, not the new HAL9000.

We might think of the shifting relationship of human beings to the natural world in the terms offered by German sociologist Gerd-Günter Voß, who has traced our movement through three different models of the “conduct of life.”

The first, and for much of human history the only conduct of life, is what he calls the traditional. Your actions within the traditional conduct of life proceed from social and familial circumstances, from what is thus handed down to you. In such a world it is reasonable for family names to be associated with trades, trades that will be passed down from father to son: Smith, Carpenter, Miller.

But the rise of the various forces that we call “modernity” led to the emergence of the strategic conduct of life: a life with a plan, with certain goals — to get into law school, to become a cosmetologist, to get a corner office.

thanks largely to totalizing technology’s formation of a world in which, to borrow a phrase from Marx and Engels, “all that is solid melts into air,” the strategic model of conduct is replaced by the situational. Instead of being systematic planners, we become agile improvisers: If the job market is bad for your college major, you turn a side hustle into a business. But because you know that your business may get disrupted by the tech industry, you don’t bother thinking long-term; your current gig might disappear at any time, but another will surely present itself, which you will assess upon its arrival.

The movement through these three forms of conduct, whatever benefits it might have, makes our relations with nature increasingly instrumental. We can see this shift more clearly when looking at our changing experience of time

Within the traditional conduct of life, it is necessary to take stewardly care of the resources required for the exercise of a craft or a profession, as these get passed on from generation to generation.

But in the progression from the traditional to the strategic to the situational conduct of life, continuity of preservation becomes less valuable than immediacy of appropriation: We need more lithium today, and merely hope to find greater reserves — or a suitable replacement — tomorrow. This revaluation has the effect of shifting the place of the natural order from something intrinsic to our practices to something extrinsic. The whole of nature becomes what economists tellingly call an externality.

The basic argument of the SCT goes like this. We live in a technopoly, a society in which powerful technologies come to dominate the people they are supposed to serve, and reshape us in their image. These technologies, therefore, might be called prescriptive (to use Franklin’s term) or manipulatory (to use Illich’s). For example, social networks promise to forge connections — but they also encourage mob rule.

all things increasingly present themselves to us as technological: we see them and treat them as what Heidegger calls a “standing reserve,” supplies in a storeroom, as it were, pieces of inventory to be ordered and conscripted, assembled and disassembled, set up and set aside

In his exceptionally ambitious book The Question Concerning Technology in China (2016) and in a series of related essays and interviews, Hui argues, as the title of his book suggests, that we go wrong when we assume that there is one question concerning technology, the question, that is universal in scope and uniform in shape. Perhaps the questions are different in Hong Kong than in the Black Forest. Similarly, the distinction Heidegger draws between ancient and modern technology — where with modern technology everything becomes a mere resource — may not universally hold.

Thesis: Technology is an anthropological universal, understood as an exteriorization of memory and the liberation of organs, as some anthropologists and philosophers of technology have formulated it; Antithesis: Technology is not anthropologically universal; it is enabled and constrained by particular cosmologies, which go beyond mere functionality or utility. Therefore, there is no one single technology, but rather multiple cosmotechnics.

I think Hui’s cosmotechnics, generously leavened with the ironic humor intrinsic to Daoism, provides a genuine Way — pun intended — beyond the limitations of the Standard Critique of Technology. I say this even though I am not a Daoist; I am, rather, a Christian. But it should be noted that Daoism is both daojiao, an organized religion, and daojia, a philosophical tradition. It is daojia that Hui advocates, which makes the wisdom of Daoism accessible and attractive to a Christian like me. Indeed, I believe that elements of daojia are profoundly consonant with Christianity, and yet underdeveloped in the Christian tradition, except in certain modes of Franciscan spirituality, for reasons too complex to get into here.

this technological Daoism as an embodiment of daojia, is accessible to people of any religious tradition or none. It provides a comprehensive and positive account of the world and one’s place in it that makes a different approach to technology more plausible and compelling. The SCT tends only to gesture in the direction of a model of human flourishing, evokes it mainly by implication, whereas Yuk Hui’s Daoist model gives an explicit and quite beautiful account.

The application of Daoist principles is most obvious, as the above exposition suggests, for “users” who would like to graduate to the status of “non-users”: those who quietly turn their attention to more holistic and convivial technologies, or who simply sit or walk contemplatively. But in the interview I quoted from earlier, Hui says, “Some have quipped that what I am speaking about is Daoist robots or organic AI” — and this needs to be more than a quip. Peter Thiel’s longstanding attempt to make everyone a disciple of René Girard is a dead end. What we need is a Daoist culture of coders, and people devoted to “action without acting” making decisions about lithium mining.

Tools that do not contribute to the Way will neither be worshipped nor despised. They will simply be left to gather dust as the people choose the tools that will guide them in the path of contentment and joy: utensils to cook food, devices to make clothes. Of course, the food of one village will differ from that of another, as will the clothing. Those who follow the Way will dwell among the “ten thousand things” of this world — what we call nature — in a certain manner that cannot be specified legally: Verse 18 of the Tao says that when virtue arises only from rules, that is a sure sign that the Way is not present and active. A cosmotechnics is a living thing, always local in the specifics of its emergence in ways that cannot be specified in advance.

It is from the ten thousand things that we learn how to live among the ten thousand things; and our choice of tools will be guided by what we have learned from that prior and foundational set of relations. This is cosmotechnics.

Multiplicity avoids the universalizing, totalizing character of technopoly. The adherents of technopoly, Hui writes, “wishfully believ[e] that the world process will stamp out differences and diversities” and thereby achieve a kind of techno-secular “theodicy,” a justification of the ways of technopoly to its human subjects. But the idea of multiple cosmotechnics is also necessary, Hui believes, in order to avoid the simply delusional attempt to find “a way out of modernity” by focusing on the indigenous or biological “Other.” An aggressive hostility to modernity and a fetishizing of pre-modernity is not the Daoist way.

“I believe that to overcome modernity without falling back into war and fascism, it is necessary to reappropriate modern technology through the renewed framework of a cosmotechnics.” His project “doesn’t refuse modern technology, but rather looks into the possibility of different technological futures.”

“Thinking rooted in the earthy virtue of place is the motor of cosmotechnics. However, for me, this discourse on locality doesn’t mean a refusal of change and of progress, or any kind of homecoming or return to traditionalism; rather, it aims at a re-appropriation of technology from the perspective of the local and a new understanding of history.”

Always Coming Home illustrates cosmotechnics in a hundred ways. Consider, for instance, information storage and retrieval. At one point we meet the archivist of the Library of the Madrone Lodge in the village of Wakwaha-na. A visitor from our world is horrified to learn that while the library gives certain texts and recordings to the City of Mind, some of their documents they simply destroy. “But that’s the point of information storage and retrieval systems! The material is kept for anyone who wants or needs it. Information is passed on — the central act of human culture.” But that is not how the librarian thinks about it. “Tangible or intangible, either you keep a thing or you give it. We find it safer to give it” — to practice “unhoarding.”

It is not information, but relation. This too is cosmotechnics.

a cosmotechnics is the point at which a way of life is realized through making. The point may be illustrated with reference to an ancient tale Hui offers, about an excellent butcher who explains to a duke what he calls the Dao, or “way,” of butchering. The reason he is a good butcher, he says, it not his mastery of a skill, or his reliance on superior tools. He is a good butcher because he understands the Dao: Through experience he has come to rely on his intuition to thrust the knife precisely where it does not cut through tendons or bones, and so his knife always stays sharp. The duke replies: “Now I know how to live.” Hui explains that “it is thus the question of ‘living,’ rather than that of technics, that is at the center of the story.”

Once a company becomes an industry leader, its employees, from top to bottom, start thinking defensively. Suddenly, people feel they have more to lose from challenging the status quo than upending it. As a result, one-time revolutionaries turn into reactionaries. Proof of this about-face comes when senior executives troop off to Washington or Brussels to lobby against changes that would make life easier for the new up and comers.

Years of continuous improvement produce an ultra-efficient business system—one that’s highly optimized, and also highly inflexible. Successful businesses are usually good at doing one thing, and one thing only. Over-specialization kills adaptability—but this is a tough to trap to avoid, since the defenders of the status quo will always argue that eking out another increment of efficiency is a safer bet than striking out in a new direction.

Long-tenured executives develop a deep base of industry experience and find it hard to question cherished beliefs. In successful companies, managers usually have a fine-grained view of “how the industry works,” and tend to discount data that would challenge their assumptions. Over time, mental models become hard-wired—a fact that makes industry stalwarts vulnerable to new rules. This risk is magnified when senior executives dominate internal conversations about future strategy and direction.

With success comes bulk—more employees, more cash and more market power. Trouble is, a resource advantage tends to make executives intellectually lazy—they start believing that success comes from outspending one’s rivals rather than from outthinking them. In practice, superior resources seldom defeat a superior strategy. So when resources start substituting for creativity, it’s time to short the shares.

One quick suggestion: Treat every belief you have about your business as nothing more than a hypothesis, forever open to disconfirmation. Being paranoid is good, becoming skeptical about your own beliefs is better.

Some of the topics I touch on: Why I believe Vision Pro may be an over-engineered “devkit” The genius & audacity behind some of Apple’s hardware decisions Gaze & pinch is an incredible UI superpower and major industry ah-ha moment Why the Vision Pro software/content story is so dull and unimaginative Why most people won’t use Vision Pro for watching TV/movies Apple’s bet in immersive video is a total game-changer for live sports Why I returned my Vision Pro… and my Top 10 wishlist to reconsider Apple’s VR debut is the best thing that ever happened to Oculus/Meta My unsolicited product advice to Meta for Quest Pro 2 and beyond

Apple really played it safe in the design of this first VR product by over-engineering it. For starters, Vision Pro ships with more sensors than what’s likely necessary to deliver Apple’s intended experience. This is typical in a first-generation product that’s been under development for so many years. It makes Vision Pro start to feel like a devkit.

A sensor party: 6 tracking cameras, 2 passthrough cameras, 2 depth sensors(plus 4 eye-tracking cameras not shown)

it’s easy to understand two particularly important decisions Apple made for the Vision Pro launch: Designing an incredible in-store Vision Pro demo experience, with the primary goal of getting as many people as possible to experience the magic of VR through Apple’s lenses — most of whom have no intention to even consider a $4,000 purchase. The demo is only secondarily focused on actually selling Vision Pro headsets. Launching an iconic woven strap that photographs beautifully even though this strap simply isn’t comfortable enough for the vast majority of head shapes. It’s easy to conclude that this decision paid off because nearly every bit of media coverage (including and especially third-party reviews on YouTube) uses the woven strap despite the fact that it’s less comfortable than the dual loop strap that’s “hidden in the box”.

Apple’s relentless and uncompromising hardware insanity is largely what made it possible for such a high-res display to exist in a VR headset, and it’s clear that this product couldn’t possibly have launched much sooner than 2024 for one simple limiting factor — the maturity of micro-OLED displays plus the existence of power-efficient chipsets that can deliver the heavy compute required to drive this kind of display (i.e. the M2).

The “hard tech” in AI are the LLMs available for rent from OpenAI, Anthropic, Cohere, and others, or available as open source with Llama, Bloom, Mistral and others. The hard-tech is a level playing field; startups do not have an advantage over incumbents.

There can be differentiation in prompt engineering, problem break-down, use of vector databases, and more. However, this isn’t something where startups have an edge, such as being willing to take more risks or be more creative. At best, it is neutral; certainly not an advantage.

This doesn’t mean it’s impossible for a startup to succeed; surely many will. It means that you need a strategy that creates differentiation and distribution, even more quickly and dramatically than is normally required

Whether you’re training existing models, developing models from scratch, or simply testing theories, high-quality data is crucial. Incumbents have the data because they have the customers. They can immediately leverage customers’ data to train models and tune algorithms, so long as they maintain secrecy and privacy.

Intercom’s AI strategy is built on the foundation of hundreds of millions of customer interactions. This gives them an advantage over a newcomer developing a chatbot from scratch. Similarly, Google has an advantage in AI video because they own the entire YouTube library. GitHub has an advantage with Copilot because they trained their AI on their vast code repository (including changes, with human-written explanations of the changes).

While there will always be individuals preferring the startup environment, the allure of working on AI at an incumbent is equally strong for many, especially pure computer and data scientsts who, more than anything else, want to work on interesting AI projects. They get to work in the code, with a large budget, with all the data, with above-market compensation, and a built-in large customer base that will enjoy the fruits of their labor, all without having to do sales, marketing, tech support, accounting, raising money, or anything else that isn’t the pure joy of writing interesting code. This is heaven for many.

A chatbot is in the chatbot market, and an SEO tool is in the SEO market. Adding AI to those tools is obviously a good idea; indeed companies who fail to add AI will likely become irrelevant in the long run. Thus we see that “AI” is a new tool for developing within existing markets, not itself a new market (except for actual hard-tech AI companies).

AI is in the solution-space, not the problem-space, as we say in product management. The customer problem you’re solving is still the same as ever. The problem a chatbot is solving is the same as ever: Talk to customers 24/7 in any language. AI enables completely new solutions that none of us were imagining a few years ago; that’s what’s so exciting and truly transformative. However, the customer problems remain the same, even though the solutions are different

Companies will pay more for chatbots where the AI is excellent, more support contacts are deferred from reaching a human, more languages are supported, and more kinds of questions can be answered, so existing chatbot customers might pay more, which grows the market. Furthermore, some companies who previously (rightly) saw chatbots as a terrible customer experience, will change their mind with sufficiently good AI, and will enter the chatbot market, which again grows that market.

the right way to analyze this is not to say “the AI market is big and growing” but rather: “Here is how AI will transform this existing market.” And then: “Here’s how we fit into that growth.”

Strong technologies capture the imaginations of technology enthusiasts. That is why many important technologies start out as weekend hobbies. Enthusiasts vote with their time, and, unlike most of the business world, have long-term horizons. They build from first principles, making full use of the available resources to design technologies as they ought to exist.

The criticisms I have been seeing about the features of the Pixel 8, however, feel like we are only repeating the kinds of fears of nearly two hundred years. We have not been able to wholly trust photographs pretty much since they were invented. The only things which have changed in that time are the ease with which the manipulations can happen, and their availability.

We all live with a growing sense that everything around us is fraudulent. It is striking to me how these tools have been introduced as confidence in institutions has declined. It feels like a death spiral of trust — not only are we expected to separate facts from their potentially misleading context, we increasingly feel doubtful that any experts are able to help us, yet we keep inventing new ways to distort reality.

The questions that are being asked of the Pixel 8’s image manipulation capabilities are good and necessary because there are real ethical implications. But I think they need to be more fully contextualized. There is a long trail of exactly the same concerns and, to avoid repeating ourselves yet again, we should be asking these questions with that history in mind. This era feels different. I think we should be asking more precisely why that is.

The questions we ask about generative technologies should acknowledge that we already have plenty of ways to lie, and that lots of the information we see is suspect. That does not mean we should not believe anything, but it does mean we ought to be asking questions about what is changed when tools like these become more widespread and easier to use.

We still believe that there will be a separation between the “application layer” companies and foundation model providers, with model companies specializing in scale and research and application layer companies specializing in product and UI. In reality, that separation hasn’t cleanly happened yet. In fact, the most successful user-facing applications out of the gate have been vertically integrated.

We predicted that the best generative AI companies could generate a sustainable competitive advantage through a data flywheel: more usage → more data → better model → more usage. While this is still somewhat true, especially in domains with very specialized and hard-to-get data, the “data moats” are on shaky ground: the data that application companies generate does not create an insurmountable moat, and the next generations of foundation models may very well obliterate any data moats that startups generate. Rather, workflows and user networks seem to be creating more durable sources of competitive advantage.

Some of the best consumer companies have 60-65% DAU/MAU; WhatsApp’s is 85%. By contrast, generative AI apps have a median of 14% (with the notable exception of Character and the “AI companionship” category). This means that users are not finding enough value in Generative AI products to use them every day yet.

generative AI’s biggest problem is not finding use cases or demand or distribution, it is proving value. As our colleague David Cahn writes, “the $200B question is: What are you going to use all this infrastructure to do? How is it going to change people’s lives?”

Netflix could buy 10 top quality screenplays per year with the cash they’ll spend on that one job.  They must have big plans for AI.There are also a half dozen AI job openings at Disney. And the tech-based streamers (Apple, Amazon) already have made big investments in AI. Sony launched an AI business unit in April 2020—in order to “enhance human imagination and creativity, particularly in the realm of entertainment.”

When Spotify launched on the stock exchange in 2018, it was losing around $30 million per month. Now it’s much larger, and is losing money at the pace of more than $100 million per month.

But the real problem at Spotify isn’t just convincing people to pay more. It runs much deeper. Spotify finds itself in the awkward position of asking people to pay more for a lousy interface that degrades the entire user experience.

Boredom is built into the platform, because they lose money if you get too excited about music—you’re like the person at the all-you-can-eat buffet who goes back for a third helping. They make the most money from indifferent, lukewarm fans, and they created their interface with them in mind. In other words, Spotify’s highest aspiration is to be the Applebee’s of music.

They need to prepare for a possible royalty war against record labels and musicians—yes, that could actually happen—and they do that by creating a zombie world of brain dead listeners who don’t even know what artist they’re hearing. I know that sounds extreme, but spend some time on the platform and draw your own conclusions.

With the comprehensive application of Artificial Intelligence into the creation and post production of images, it seems questionable if the resulting visualisations can still be considered ‘photographs’ in a classical sense – drawing with light. Automation has been part of the popular strain of photography since its inception, but even the amateurs with only basic knowledge of the craft could understand themselves as author of their images. We state a legitimation crisis for the current usage of the term. This paper is an invitation to consider Synthography as a term for a new genre for image production based on AI, observing the current occurrence and implementation in consumer cameras and post-production.

The proliferation of AI-generated images in online environments won’t eradicate human art wholesale, but it does represent a reshuffling of the market incentives that help creative economies flourish. Like the college essay, another genre of human creativity threatened by AI usurpation, creative “products” might become more about process than about art as a commodity.

Are artists using computer software on iPads to make seemingly hand-painted images engaged in a less creative process than those who produce the image by hand? We can certainly judge one as more meritorious than the other but claiming that one is more original is harder to defend.

An understanding of the technology as one that separates human from machine into distinct categories leaves little room for the messier ways we often fit together with our tools. AI-generated images will have a big impact on copyright law, but the cultural backlash against the “computers making art” overlooks the ways computation has already been incorporated into the arts.

The problem with debates around AI-generated images that demonize the tool is that the displacement of human-made art doesn’t have to be an inevitability. Markets can be adjusted to mitigate unemployment in changing economic landscapes. As legal scholar Ewan McGaughey points out, 42% of English workers were redundant after WWII — and yet the U.K. managed to maintain full employment.

Contemporary critics claim that prompt engineering and synthography aren’t emergent professions but euphemisms necessary to equate AI-generated artwork with the work of human artists. As with the development of photography as a medium, today’s debates about AI often overlook how conceptions of human creativity are themselves shaped by commercialization and labor.

Others looking to elevate AI art’s status alongside other forms of digital art are opting for an even loftier rebrand: “synthography.” This categorization suggests a process more complex than the mechanical operation of a picture-making tool, invoking the active synthesis of disparate aesthetic elements. Like Fox Talbot and his contemporaries in the nineteenth century, “synthographers” maintain that AI art simply automates the most time-consuming parts of drawing and painting, freeing up human cognition for higher-order creativity.

Separating human from camera was a necessary part of preserving the myth of the camera as an impartial form of vision. To incorporate photography into an economic landscape of creativity, however, human agency needed to ascribe to all parts of the process.

Consciously or not, proponents of AI-generated images stamp the tool with rhetoric that mirrors the democratic aspirations of the twenty-first century.

Stability AI took a similar tack, billing itself as “AI by the people, for the people,” despite turning Stable Diffusion, their text-to-image model, into a profitable asset. That the program is easy to use is another selling point. Would-be digital artists no longer need to use expensive specialized software to produce visually interesting material.

Meanwhile, communities of digital artists and their supporters claim that the reason AI-generated images are compelling at all is because they were trained with data sets that contained copyrighted material. They reject the claim that AI-generated art produces anything original and suggest it instead be thought of as a form of “twenty-first century collage.”

Erasing human influence from the photographic process was good for underscoring arguments about objectivity, but it complicated commercial viability. Ownership would need to be determined if photographs were to circulate as a new form of property. Was the true author of a photograph the camera or its human operator?

By reframing photographs as les dessins photographiques — or photographic drawings, the plaintiffs successfully established that the development of photographs in a darkroom was part of an operator’s creative process. In addition to setting up a shot, the photographer needed to coax the image from the camera’s film in a process resembling the creative output of drawing. The camera was a pencil capable of drawing with light and photosensitive surfaces, but held and directed by a human author.

Establishing photography’s dual function as both artwork and document may not have been philosophically straightforward, but it staved off a surge of harder questions.

Human intervention in the photographic process still appeared to happen only on the ends — in setup and then development — instead of continuously throughout the image-making process.

Software is misunderstood. It can feel like a discrete thing, something with which we interact. But, really, it is the intrusion into our world of something very alien. It is the strange interaction of electricity, semiconductors, and instructions, all of which somehow magically control objects that range from screens to robots to phones, to medical devices, laptops, and a bewildering multitude of other things. It is almost infinitely malleable, able to slide and twist and contort itself such that, in its pliability, it pries open doorways as yet unseen.

the clearing price for software production will change. But not just because it becomes cheaper to produce software. In the limit, we think about this moment as being analogous to how previous waves of technological change took the price of underlying technologies—from CPUs, to storage and bandwidth—to a reasonable approximation of zero, unleashing a flood of speciation and innovation. In software evolutionary terms, we just went from human cycle times to that of the drosophila: everything evolves and mutates faster.

A software industry where anyone can write software, can do it for pennies, and can do it as easily as speaking or writing text, is a transformative moment. It is an exaggeration, but only a modest one, to say that it is a kind of Gutenberg moment, one where previous barriers to creation—scholarly, creative, economic, etc—are going to fall away, as people are freed to do things only limited by their imagination, or, more practically, by the old costs of producing software.

We have almost certainly been producing far less software than we need. The size of this technical debt is not knowable, but it cannot be small, so subsequent growth may be geometric. This would mean that as the cost of software drops to an approximate zero, the creation of software predictably explodes in ways that have barely been previously imagined.

Entrepreneur and publisher Tim O’Reilly has a nice phrase that is applicable at this point. He argues investors and entrepreneurs should “create more value than you capture.” The technology industry started out that way, but in recent years it has too often gone for the quick win, usually by running gambits from the financial services playbook. We think that for the first time in decades, the technology industry could return to its roots, and, by unleashing a wave of software production, truly create more value than its captures.

Software production has been too complex and expensive for too long, which has caused us to underproduce software for decades, resulting in immense, society-wide technical debt.

technology has a habit of confounding economics. When it comes to technology, how do we know those supply and demand lines are right? The answer is that we don’t. And that’s where interesting things start happening. Sometimes, for example, an increased supply of something leads to more demand, shifting the curves around. This has happened many times in technology, as various core components of technology tumbled down curves of decreasing cost for increasing power (or storage, or bandwidth, etc.).

Suddenly AI has become cheap, to the point where people are “wasting” it via “do my essay” prompts to chatbots, getting help with microservice code, and so on. You could argue that the price/performance of intelligence itself is now tumbling down a curve, much like as has happened with prior generations of technology.

it’s worth reminding oneself that waves of AI enthusiasm have hit the beach of awareness once every decade or two, only to recede again as the hyperbole outpaces what can actually be done.

To write about something hard to explain, write a detailed letter to a friend about why it is so hard to explain, and then remove the initial “Dear Friend” part and you’ll have a great first draft.

To be interesting just tell your story with uncommon honesty.

Most articles and stories are improved significantly if you delete the first page of the manuscript draft. Immediately start with the action.

Each technology can not stand alone. It takes a saw to make a hammer and it takes a hammer to make a saw. And it takes both tools to make a computer, and in today’s factory it takes a computer to make saws and hammers. This co-dependency creates an ecosystem of highly interdependent technologies that support each other

On the other hand, I see this technium as an extension of the same self-organizing system responsible for the evolution of life on this planet. The technium is evolution accelerated. A lot of the same dynamics that propel evolution are also at work in the technium

Our technologies are ultimately not contrary to life, but are in fact an extension of life, enabling it to develop yet more options and possibilities at a faster rate. Increasing options and possibilities is also known as progress, so in the end, what the technium brings us humans is progress.

Libraries, journals, communication networks, and the accumulation of other technologies help create the next idea, beyond the efforts of a single individual

We also see near-identical parallel inventions of tricky contraptions like slingshots and blowguns. However, because it was so ancient, we don’t have a lot of data for this behavior. What we would really like is to have a N=100 study of hundreds of other technological civilizations in our galaxy. From that analysis we’d be able to measure, outline, and predict the development of technologies. That is a key reason to seek extraterrestrial life.

When information is processed in a computer, it is being ceaselessly replicated and re-copied while it computes. Information wants to be copied. Therefore, when certain people get upset about the ubiquitous copying happening in the technium, their misguided impulse is to stop the copies. They want to stamp out rampant copying in the name of "copy protection,” whether it be music, science journals, or art for AI training. But the emergent behavior of the technium is to copy promiscuously. To ban, outlaw, or impede the superconductivity of copies is to work against the grain of the system.

the worry of some environmentalists is that technology can only contribute more to the problem and none to the solution. They believe that tech is incapable of being green because it is the source of relentless consumerism at the expense of diminishing nature, and that our technological civilization requires endless growth to keep the system going. I disagree.

Over time evolution arranges the same number of atoms in more complex patterns to yield more complex organisms, for instance producing an agile lemur the same size and weight as a jelly fish. We seek the same shift in the technium. Standard economic growth aims to get consumers to drink more wine. Type 2 growth aims to get them to not drink more wine, but better wine.

to measure (and thus increase) productivity we count up the number of refrigerators manufactured and sold each year. More is generally better. But this counting tends to overlook the fact that refrigerators have gotten better over time. In addition to making cold, they now dispense ice cubes, or self-defrost, and use less energy. And they may cost less in real dollars. This betterment is truly real value, but is not accounted for in the “more” column

it is imperative that we figure out how to shift more of our type 1 growth to type 2 growth, because we won’t be able to keep expanding the usual “more.”  We will have to perfect a system that can keep improving and getting better with fewer customers each year, smaller markets and audiences, and fewer workers. That is a huge shift from the past few centuries where every year there has been more of everything.

“degrowthers” are correct in that there are limits to bulk growth — and running out of humans may be one of them. But they don’t seem to understand that evolutionary growth, which includes the expansion of intangibles such as freedom, wisdom, and complexity, doesn’t have similar limits. We can always figure out a way to improve things, even without using more stuff — especially without using more stuff!

the technium is not inherently contrary to nature; it is inherently derived from evolution and thus inherently capable of being compatible with nature. We can choose to create versions of the technium that are aligned with the natural world.

Social media can transmit false information at great range at great speed. But compared to what? Social media's influence on elections from transmitting false information was far less than the influence of the existing medias of cable news and talk radio, where false information was rampant. Did anyone seriously suggest we should regulate what cable news hosts or call in radio listeners could say? Bullying middle schoolers on social media? Compared to what? Does it even register when compared to the bullying done in school hallways? Radicalization on YouTube? Compared to talk radio? To googling?

Kids are inherently obsessive about new things, and can become deeply infatuated with stuff that they outgrow and abandon a few years later. So the fact they may be infatuated with social media right now should not in itself be alarming. Yes, we should indeed understand how it affects children and how to enhance its benefits, but it is dangerous to construct national policies for a technology based on the behavior of children using it.

Since it is the same technology, inspecting how it is used in other parts of the world would help us isolate what is being caused by the technology and what is being caused by the peculiar culture of the US.

You don’t notice what difference you make because of the platform's humongous billions-scale. In aggregate your choices make a difference which direction it — or any technology — goes. People prefer to watch things on demand, so little by little, we have steered the technology to let us binge watch. Streaming happened without much regulation or even enthusiasm of the media companies. Street usage is the fastest and most direct way to steer tech.

Vibrators instead of the cacophony of ringing bells on cell phones is one example of a marketplace technological solution

The long-term effects of AI will affect our society to a greater degree than electricity and fire, but its full effects will take centuries to play out. That means that we’ll be arguing, discussing, and wrangling with the changes brought about by AI for the next 10 decades. Because AI operates so close to our own inner self and identity, we are headed into a century-long identity crisis.

What we tend to call AI, will not be considered AI years from now

What we are discovering is that many of the cognitive tasks we have been doing as humans are dumber than they seem. Playing chess was more mechanical than we thought. Playing the game Go is more mechanical than we thought. Painting a picture and being creative was more mechanical than we thought. And even writing a paragraph with words turns out to be more mechanical than we thought

out of the perhaps dozen of cognitive modes operating in our minds, we have managed to synthesize two of them: perception and pattern matching. Everything we’ve seen so far in AI is because we can produce those two modes. We have not made any real progress in synthesizing symbolic logic and deductive reasoning and other modes of thinking

we are slowly realizing we still have NO IDEA how our own intelligences really work, or even what intelligence is. A major byproduct of AI is that it will tell us more about our minds than centuries of psychology and neuroscience have

There is no monolithic AI. Instead there will be thousands of species of AIs, each engineered to optimize different ways of thinking, doing different jobs

Now from the get-go we assume there will be significant costs and harms of anything new, which was not the norm in my parent's generation

The astronomical volume of money and greed flowing through this frontier overwhelmed and disguised whatever value it may have had

The sweet elegance of blockchain enables decentralization, which is a perpetually powerful force. This tech just has to be matched up to the tasks — currently not visible — where it is worth paying the huge cost that decentralization entails. That is a big ask, but taking the long-view, this moment may not be a failure

My generic career advice for young people is that if at all possible, you should aim to work on something that no one has a word for. Spend your energies where we don’t have a name for what you are doing, where it takes a while to explain to your mother what it is you do. When you are ahead of language, that means you are in a spot where it is more likely you are working on things that only you can do. It also means you won’t have much competition.

Your 20s are the perfect time to do a few things that are unusual, weird, bold, risky, unexplainable, crazy, unprofitable, and looks nothing like “success.” The less this time looks like success, the better it will be as a foundation

How much of that is the actual photo and how much you might consider to be synthesized is a line I think each person draws for themselves. I think it depends on the context; Moon photography makes for a neat demo but it is rarely relevant. A better question is whether these kinds of software enhancements hallucinate errors along the same lines of what happened in Xerox copiers for years.

here are five large language model applications that I find intriguing: Intelligent automation starting with browsers but this feels like a step towards phenotropics Text generation when this unlocks new UIs like Word turning into Photoshop or something Human-machine interfaces because you can parse intent instead of nouns When meaning can be interfaced with programmatically and at ludicrous scale Anything that exploits the inhuman breadth of knowledge embedded in the model, because new knowledge is often the collision of previously separated old knowledge, and this has not been possible before.

Even if chatbots were to fix their accuracy issues, Google would still have a business model problem to contend with. The company makes money when people click ads next to search results, and it’s awkward to fit ads into conversational replies. Imagine receiving a response and then immediately getting pitched to go somewhere else — it feels slimy, and unhelpful. Google thus has little incentive to move us beyond traditional search, at least not in a paradigm-shifting way, until it figures out how to make the money aspect work. In the meantime, it’ll stick with the less impressive Google Assistant.

“Google doesn’t inherently want you, at an inherent level, to just get the answer to every problem. Because that might reduce the need to go click around the web, which would then reduce the need for us to go to Google.”

Web3 has largely been technology looking for problems to solve while generative AI has been about almost too many solutions created by technology which is evolving on a seemingly daily basis. As a result, web3 has thus far been evangelists trying to convince us to re-solve old problems with their new technology

We fully acknowledge that it’s hard to be confident in any predictions at the pace the field is moving. Right now, though, it seems we’re much more likely to see applications full of creative images created strictly by programmers than applications with human-designed art built strictly by creators.

the concept of file folders and directories, essential to previous generations’ understanding of computers, is gibberish to many modern students.

be inspired by the untapped potential of human capabilities. Don't just extrapolate yesterday's technology and then cram people into it.